TELKOMNIKA Telecommunication, Computing, Electronics and Control
Towards more accurate and efficient human iris recognition model using deep learning technology
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Towards more accurate and efficient human iris recognition model using deep learning technology
Towards more accurate and efficient human iris recognition model using deep learning technology
Subject
Biometric security systems, Convolutional neural network, Deep learning, Histogram equalization techniques, Iris recognition
Description
In this study, an end-to-end human iris recognition system is presented to automatically identify individuals for high level of security purposes.
The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a
consumption time of 17.59 minutes for training set. While the testing
accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.
The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a
consumption time of 17.59 minutes for training set. While the testing
accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.
Creator
Bashra Kadhim Oleiwi Chabor Alwawi, Ali Fadhil Yaseen Althabhawee
Source
DOI: 10.12928/TELKOMNIKA.v20i4.23759
Publisher
Universitas Ahmad Dahlan
Date
August 2022
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Bashra Kadhim Oleiwi Chabor Alwawi, Ali Fadhil Yaseen Althabhawee, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Towards more accurate and efficient human iris recognition model using deep learning technology,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/4398.
Towards more accurate and efficient human iris recognition model using deep learning technology,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/4398.